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Melton CA, White BM, Davis RL, Bednarczyk RA, Shaban-Nejad A. Fine-Tuned Sentiment Analysis of COVID-19 Vaccine Related Social Media Data: A Comparative Study. J Med Internet Res 2022; 24:e40408. [PMID: 36174192 PMCID: PMC9578521 DOI: 10.2196/40408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 08/18/2022] [Accepted: 09/15/2022] [Indexed: 11/18/2022] Open
Abstract
Background The emergence of the novel coronavirus (COVID-19) and the necessary separation of populations have led to an unprecedented number of new social media users seeking information related to the pandemic. Currently, with an estimated 4.5 billion users worldwide, social media data offer an opportunity for near real-time analysis of large bodies of text related to disease outbreaks and vaccination. These analyses can be used by officials to develop appropriate public health messaging, digital interventions, educational materials, and policies. Objective Our study investigated and compared public sentiment related to COVID-19 vaccines expressed on 2 popular social media platforms—Reddit and Twitter—harvested from January 1, 2020, to March 1, 2022. Methods To accomplish this task, we created a fine-tuned DistilRoBERTa model to predict the sentiments of approximately 9.5 million tweets and 70 thousand Reddit comments. To fine-tune our model, our team manually labeled the sentiment of 3600 tweets and then augmented our data set through back-translation. Text sentiment for each social media platform was then classified with our fine-tuned model using Python programming language and the Hugging Face sentiment analysis pipeline. Results Our results determined that the average sentiment expressed on Twitter was more negative (5,215,830/9,518,270, 54.8%) than positive, and the sentiment expressed on Reddit was more positive (42,316/67,962, 62.3%) than negative. Although the average sentiment was found to vary between these social media platforms, both platforms displayed similar behavior related to the sentiment shared at key vaccine-related developments during the pandemic. Conclusions Considering this similar trend in shared sentiment demonstrated across social media platforms, Twitter and Reddit continue to be valuable data sources that public health officials can use to strengthen vaccine confidence and combat misinformation. As the spread of misinformation poses a range of psychological and psychosocial risks (anxiety and fear, etc), there is an urgency in understanding the public perspective and attitude toward shared falsities. Comprehensive educational delivery systems tailored to a population’s expressed sentiments that facilitate digital literacy, health information–seeking behavior, and precision health promotion could aid in clarifying such misinformation.
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Affiliation(s)
- Chad A Melton
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee at Knoxville, Knoxville, US.,UTHSC-ORNL Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, US
| | - Brianna M White
- UTHSC-ORNL Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, US
| | - Robert L Davis
- UTHSC-ORNL Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, US
| | - Robert A Bednarczyk
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, US
| | - Arash Shaban-Nejad
- UTHSC-ORNL Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, 50 N Dunlap Street, 492R, Memphis, US
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Melton CA, Olusanya OA, Ammar N, Shaban-Nejad A. Sentiment Analysis of the Covid-19 Vaccines on Social Media. Stud Health Technol Inform 2022; 290:1056-1057. [PMID: 35673203 DOI: 10.3233/shti220265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The COVID-19 pandemic fueled one of the quickest vaccine developments in history. Misinformation on online social media often leads to negative vaccine sentiment. We conducted a sentiment analysis and Latent Dirichlet Allocation topic modeling from Reddit communities focusing on the COVID-19 vaccine. Polarity analysis suggested these communities expressed positive sentiment regarding the vaccine. However, topic modeling revealed community members mainly focused on the side effects and vaccination experience.
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Affiliation(s)
- Chad A Melton
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN, United States
- University of Tennessee Health Science Center-Oak-Ridge National Laboratory (UTHSC-ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, Memphis, TN, United States
| | - Olufunto A Olusanya
- University of Tennessee Health Science Center-Oak-Ridge National Laboratory (UTHSC-ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, Memphis, TN, United States
| | - Nariman Ammar
- University of Tennessee Health Science Center-Oak-Ridge National Laboratory (UTHSC-ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, Memphis, TN, United States
| | - Arash Shaban-Nejad
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN, United States
- University of Tennessee Health Science Center-Oak-Ridge National Laboratory (UTHSC-ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, Memphis, TN, United States
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Olusanya OA, White B, Melton CA, Shaban-Nejad A. Examining the Implementation of Digital Health to Strengthen the COVID-19 Pandemic Response and Recovery and Scale up Equitable Vaccine Access in African Countries. ArXiv 2022:arXiv:2206.03286v1. [PMID: 35677423 PMCID: PMC9176651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
The COVID-19 pandemic has profoundly impacted the world, having taken the lives of over 6 million individuals. Accordingly, this pandemic has caused a shift in conversations surrounding the burden of diseases worldwide, welcoming insights from multidisciplinary fields including digital health and artificial intelligence. Africa faces a heavy disease burden that exacerbates the current COVID-19 pandemic and limits the scope of public health preparedness, response, containment, and case management. Herein, we examined the potential impact of transformative digital health technologies in mitigating the global health crisis with reference to African countries. Furthermore, we proposed recommendations for scaling up digital health technologies and artificial intelligence-based platforms to tackle the transmission of the SARS-CoV-2 and enable equitable vaccine access. Challenges related to the pandemic are numerous. Rapid response and management strategies-that is, contract tracing, case surveillance, diagnostic testing intensity, and most recently vaccine distribution mapping-can overwhelm the health care delivery system that is fragile. Although challenges are vast, digital health technologies can play an essential role in achieving sustainable resilient recovery and building back better. It is plausible that African nations are better equipped to rapidly identify, diagnose, and manage infected individuals for COVID-19, other diseases, future outbreaks, and pandemics.
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Affiliation(s)
- Olufunto A Olusanya
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, United States
| | - Brianna White
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, United States
| | - Chad A Melton
- Bredesen Center for Interdisciplinary Research and Graduate Education, The University of Tennessee, Knoxville, TN, United States
| | - Arash Shaban-Nejad
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, United States
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Olusanya OA, White B, Melton CA, Shaban-Nejad A. Examining the Implementation of Digital Health to Strengthen the COVID-19 Pandemic Response and Recovery and Scale up Equitable Vaccine Access in African Countries. JMIR Form Res 2022; 6:e34363. [PMID: 35512271 PMCID: PMC9116456 DOI: 10.2196/34363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 03/08/2022] [Accepted: 04/21/2022] [Indexed: 12/01/2022] Open
Abstract
The COVID-19 pandemic has profoundly impacted the world, having taken the lives of over 6 million individuals. Accordingly, this pandemic has caused a shift in conversations surrounding the burden of diseases worldwide, welcoming insights from multidisciplinary fields including digital health and artificial intelligence. Africa faces a heavy disease burden that exacerbates the current COVID-19 pandemic and limits the scope of public health preparedness, response, containment, and case management. Herein, we examined the potential impact of transformative digital health technologies in mitigating the global health crisis with reference to African countries. Furthermore, we proposed recommendations for scaling up digital health technologies and artificial intelligence–based platforms to tackle the transmission of the SARS-CoV-2 and enable equitable vaccine access. Challenges related to the pandemic are numerous. Rapid response and management strategies—that is, contract tracing, case surveillance, diagnostic testing intensity, and most recently vaccine distribution mapping—can overwhelm the health care delivery system that is fragile. Although challenges are vast, digital health technologies can play an essential role in achieving sustainable resilient recovery and building back better. It is plausible that African nations are better equipped to rapidly identify, diagnose, and manage infected individuals for COVID-19, other diseases, future outbreaks, and pandemics.
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Affiliation(s)
- Olufunto A Olusanya
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, United States
| | - Brianna White
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, United States
| | - Chad A Melton
- Bredesen Center for Interdisciplinary Research and Graduate Education, The University of Tennessee, Knoxville, TN, United States
| | - Arash Shaban-Nejad
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, United States
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Melton CA, Olusanya OA, Ammar N, Shaban-Nejad A. Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence. J Infect Public Health 2021; 14:1505-1512. [PMID: 34426095 PMCID: PMC8364208 DOI: 10.1016/j.jiph.2021.08.010] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 08/06/2021] [Accepted: 08/09/2021] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND The COVID-19 pandemic fueled one of the most rapid vaccine developments in history. However, misinformation spread through online social media often leads to negative vaccine sentiment and hesitancy. METHODS To investigate COVID-19 vaccine-related discussion in social media, we conducted a sentiment analysis and Latent Dirichlet Allocation topic modeling on textual data collected from 13 Reddit communities focusing on the COVID-19 vaccine from Dec 1, 2020, to May 15, 2021. Data were aggregated and analyzed by month to detect changes in any sentiment and latent topics. RESULTS Polarity analysis suggested these communities expressed more positive sentiment than negative regarding the vaccine-related discussions and has remained static over time. Topic modeling revealed community members mainly focused on side effects rather than outlandish conspiracy theories. CONCLUSION Covid-19 vaccine-related content from 13 subreddits show that the sentiments expressed in these communities are overall more positive than negative and have not meaningfully changed since December 2020. Keywords indicating vaccine hesitancy were detected throughout the LDA topic modeling. Public sentiment and topic modeling analysis regarding vaccines could facilitate the implementation of appropriate messaging, digital interventions, and new policies to promote vaccine confidence.
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Affiliation(s)
- Chad A Melton
- University of Tennessee, Bredesen Center for Interdisciplinary Research and Graduate Education, Knoxville, TN, USA
| | - Olufunto A Olusanya
- Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Nariman Ammar
- Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Arash Shaban-Nejad
- University of Tennessee, Bredesen Center for Interdisciplinary Research and Graduate Education, Knoxville, TN, USA; Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, USA.
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Melton CA, Hughes DC, Page DL, Phillips MS. Temporal multispectral and 3D analysis of Cerro de Pasco, Peru. Sci Total Environ 2020; 706:135640. [PMID: 31862591 DOI: 10.1016/j.scitotenv.2019.135640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 11/17/2019] [Accepted: 11/18/2019] [Indexed: 06/10/2023]
Abstract
Mining operations across the world often lead to contamination of land, water resources, ecosystems and in some cases, entire communities.Results of recent health and ground sampling studies revealed extensive lead contamination within the populace and around the City of Cerro de Pasco, Peru. Tailings excavated from a large open pit zinc mine in the center of the city have been aggregated in four large stockpiles within close proximity to neighborhoods, schools, and hospitals. Visual comparison of ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) imagery from 2001 and Sentinel-2 imagery from 2018 suggests a size increase in one tailing stockpile in particular near the neighborhood of Paragsha. Due to ongoing mining efforts, the hypothesis motivating the work presented here is that Pb-bearing minerals would be detectable through multispectral analysis, an increase in Pb mineral percent abundance would be observed and tailing stockpile volume would be detectable between 2001 and 2016. This hypothesis is tested using Spectral Angle Mapper (SAM), Adaptive Coherence Estimator (ACE), and Jeffries-Matusita distance calculation on ASTER (2001) and Sentinel-2 (2018) VNIR and SWIR bands. Volume and area estimate of tailing stockpiles were calculated using a photogrammetrically derived point cloud. SAM detected the presence of five Pb-bearing minerals around Cerro de Pasco and Paragsha. The results of the temporal SAM analysis displayed an increase of approximately 17% of Pb-bearing minerals around the greater Cerro de Pasco city area and approximately 11% for the neighborhood of Paragsha. Jeffries-Matusita distance results suggest clear correlation between contamination sources and affected locations. Total tailing stockpile volume was measured to be approximately 200,300,000 m3. Volume for Pile 4 was estimated to have increased by approximately 46,000,000 m3 between 2001 and 2018. These presented results will hopefully inspire and guide future remote sensing campaigns, perhaps involving a UAV or aircraft-based hyperspectral instrument.
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Affiliation(s)
- C A Melton
- University of Tennessee, Knoxville, United States of America; University of Tennessee, Bredesen Center for Interdisciplinary Research and Graduate Education, United States of America; Oak Ridge National Laboratory, United States of America.
| | - D C Hughes
- Oak Ridge National Laboratory, United States of America.
| | - D L Page
- Oak Ridge National Laboratory, United States of America.
| | - M S Phillips
- University of Tennessee, Knoxville, United States of America; University of Tennessee, Department of Earth and Planetary Sciences, United States of America.
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